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Merge pull request #176 from elder-plinius/master
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Nutrition
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kyegomez authored Nov 24, 2023
2 parents 7faa8bd + 7b0614b commit 57f19c1
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99 changes: 99 additions & 0 deletions playground/demos/nutrition/nutrition.py
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import os
import base64
import requests
from dotenv import load_dotenv
from swarms.models import Anthropic, OpenAIChat
from swarms.structs import Flow

# Load environment variables
load_dotenv()
openai_api_key = os.getenv("OPENAI_API_KEY")

# Define prompts for various tasks
MEAL_PLAN_PROMPT = "Based on the following user preferences: dietary restrictions as vegetarian, preferred cuisines as Italian and Indian, a total caloric intake of around 2000 calories per day, and an exclusion of legumes, create a detailed weekly meal plan. Include a variety of meals for breakfast, lunch, dinner, and optional snacks."
IMAGE_ANALYSIS_PROMPT = "Identify the items in this fridge, including their quantities and condition."

# Function to encode image to base64
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')

# Initialize Language Model (LLM)
llm = OpenAIChat(
openai_api_key=openai_api_key,
max_tokens=3000,
)

# Function to handle vision tasks
def create_vision_agent(image_path):
base64_image = encode_image(image_path)
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {openai_api_key}"
}
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": IMAGE_ANALYSIS_PROMPT},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
]
}
],
"max_tokens": 300
}
response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
return response.json()

# Function to generate an integrated shopping list considering meal plan and fridge contents
def generate_integrated_shopping_list(meal_plan_output, image_analysis, user_preferences):
# Prepare the prompt for the LLM
fridge_contents = image_analysis['choices'][0]['message']['content']
prompt = (f"Based on this meal plan: {meal_plan_output}, "
f"and the following items in the fridge: {fridge_contents}, "
f"considering dietary preferences as vegetarian with a preference for Italian and Indian cuisines, "
f"generate a comprehensive shopping list that includes only the items needed.")

# Send the prompt to the LLM and return the response
response = llm(prompt)
return response # assuming the response is a string

# Define agent for meal planning
meal_plan_agent = Flow(
llm=llm,
sop=MEAL_PLAN_PROMPT,
max_loops=1,
autosave=True,
saved_state_path="meal_plan_agent.json",
)

# User preferences for meal planning
user_preferences = {
"dietary_restrictions": "vegetarian",
"preferred_cuisines": ["Italian", "Indian"],
"caloric_intake": 2000,
"other notes": "Doesn't eat legumes"
}

# Generate Meal Plan
meal_plan_output = meal_plan_agent.run(
f"Generate a meal plan: {user_preferences}"
)

# Vision Agent - Analyze an Image
image_analysis_output = create_vision_agent("full_fridge.jpg")

# Generate Integrated Shopping List
integrated_shopping_list = generate_integrated_shopping_list(meal_plan_output, image_analysis_output, user_preferences)

# Print and save the outputs
print("Meal Plan:", meal_plan_output)
print("Integrated Shopping List:", integrated_shopping_list)

with open("nutrition_output.txt", "w") as file:
file.write("Meal Plan:\n" + meal_plan_output + "\n\n")
file.write("Integrated Shopping List:\n" + integrated_shopping_list + "\n")

print("Outputs have been saved to nutrition_output.txt")

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